© Springer-Verlag Berlin Heidelberg 2013. All rights are reserved. Diffusion processes are a promising instrument for realistically modelling the time-continuous evolution of phenomena not only in the natural sciences but also in finance and economics. Their mathematical theory, however, is challenging, and hence diffusion modelling is often carried out incorrectly, and the according statistical inference is considered almost exclusively by theoreticians. This book explains both topics in an illustrative way which also addresses practitioners. It provides a complete overview of the current state of research and presents important, novel insights. The theory is demonstrated using real data applications
In this PhD. Thesis we focus on diffusion models. Diffusions are very attractive and widely applied ...
Data available on continuous-time diffusions are always sampled discretely in time. In most cases, t...
In this PhD. Thesis we focus on diffusion models. Diffusions are very attractive and widely applied ...
Fuchs C. Inference for Diffusion Processes. With Applications in Life Sciences. Berlin, Heidelberg: ...
Statistical Inference for Fractional Diffusion Processes looks at statistical inference for stochast...
This paper provides methods for carrying out likelihood based inference for diffusion driven models,...
This thesis has been submitted in fulfilment of the requirements for a postgraduate degree (e.g. PhD...
The topic of the thesis is statistical inference from diffusion driven models. The the-ory of estima...
The methodological framework developed and reviewed in this article concerns the unbiased Monte Car...
Some optimal inference results for a class of diffusion processes, including the continuous state br...
This thesis consists of five papers (Paper A-E) on statistical modeling of diffusion processes. Two ...
The objective of the paper is to present a novel methodology for likelihood-based inference for disc...
Diffusion processes provide a natural way of modelling a variety of physical and economic phenomena...
We consider ergodic diffusion processes for which the class of invariant measures is an exponential ...
Diffusion processes observed partially, typically at discrete timepoints and possibly with observati...
In this PhD. Thesis we focus on diffusion models. Diffusions are very attractive and widely applied ...
Data available on continuous-time diffusions are always sampled discretely in time. In most cases, t...
In this PhD. Thesis we focus on diffusion models. Diffusions are very attractive and widely applied ...
Fuchs C. Inference for Diffusion Processes. With Applications in Life Sciences. Berlin, Heidelberg: ...
Statistical Inference for Fractional Diffusion Processes looks at statistical inference for stochast...
This paper provides methods for carrying out likelihood based inference for diffusion driven models,...
This thesis has been submitted in fulfilment of the requirements for a postgraduate degree (e.g. PhD...
The topic of the thesis is statistical inference from diffusion driven models. The the-ory of estima...
The methodological framework developed and reviewed in this article concerns the unbiased Monte Car...
Some optimal inference results for a class of diffusion processes, including the continuous state br...
This thesis consists of five papers (Paper A-E) on statistical modeling of diffusion processes. Two ...
The objective of the paper is to present a novel methodology for likelihood-based inference for disc...
Diffusion processes provide a natural way of modelling a variety of physical and economic phenomena...
We consider ergodic diffusion processes for which the class of invariant measures is an exponential ...
Diffusion processes observed partially, typically at discrete timepoints and possibly with observati...
In this PhD. Thesis we focus on diffusion models. Diffusions are very attractive and widely applied ...
Data available on continuous-time diffusions are always sampled discretely in time. In most cases, t...
In this PhD. Thesis we focus on diffusion models. Diffusions are very attractive and widely applied ...